Bottenhorn Katherine L, Salo Taylor, Riedel Michael C, Sutherland Matthew T, Robinson Jennifer L, Musser Erica D, Laird Angela R
Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA USA.
Department of Psychology, Florida International University, Miami, FL USA.
Apert Neuro. 2023;3. doi: 10.52294/001c.87572. Epub 2023 Aug 28.
Collecting physiological data during fMRI experiments can improve fMRI data cleaning and contribute to our understanding of psychophysiological processes; however, these recordings are frequently fraught with artifacts from the MRI pulse sequence. Here, we assess data from BIOPAC Systems, Inc., one of the more widely used manufacturers of physiological monitoring equipment, and evaluate their recommendations for filtering such artifacts from electrocardiogram and electrodermal activity data collected during single-band, single-echo fMRI sequences and extend these recommendations to address artifacts associated with multiband, multi-echo fMRI sequences. While the magnitude and frequencies of artifacts differ with these aspects of pulse sequences, their effects can be mitigated via application of digital filters incorporating slice collection, multiband factor, and repetition time. The implementation of these filters is provided both in interactive online notebooks and an open source denoising tool.
在功能磁共振成像（fMRI）实验中收集生理数据可以改善fMRI数据清理，并有助于我们理解心理生理过程；然而，这些记录经常充满来自MRI脉冲序列的伪影。在这里，我们评估了BIOPAC Systems公司的数据，该公司是生理监测设备使用较为广泛的制造商之一，并评估了他们关于从单波段、单回波fMRI序列期间收集的心电图和皮肤电活动数据中过滤此类伪影的建议，并扩展这些建议以解决与多波段、多回波fMRI序列相关的伪影。虽然伪影的大小和频率因脉冲序列的这些方面而有所不同，但可以通过应用结合切片采集、多波段因子和重复时间的数字滤波器来减轻它们的影响。这些滤波器的实现既在交互式在线笔记本中提供，也在一个开源去噪工具中提供。
